Evidence map›Paper›PMID 36582644›Full record

ArticleFrontiers in plant science2022

Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content.

Bo Sun, Rui Guo, Zhi Liu, Xiaolei Shi, Qing Yang, Jiayao Shi, Mengchen Zhang, Chunyan Yang, Shugang Zhao, Jie Zhang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Bo SunInstitute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China.
Rui GuoInstitute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China.
Zhi LiuInstitute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China.
Xiaolei ShiInstitute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China.
Qing YangInstitute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China.
Jiayao ShiInstitute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China.
Mengchen ZhangInstitute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China.
Chunyan YangInstitute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China.
Shugang ZhaoCollege of Life Sciences, Hebei Agricultural University, Baoding, China.
Jie ZhangCollege of Life Sciences, Hebei Agricultural University, Baoding, China.
Jianhan HeInstitute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China.
Jiaoping ZhangState Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Nanjing, China.
Jianhui SuAgricultural-Regionalization Workstation of Shijiazhuang's Gaocheng District, Shijiazhuang, China.
Qijian SongSoybean Genomics and Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States.
Long YanInstitute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Genomic selection (GS) is a potential breeding approach for soybean improvement. Methods: In this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN. Results: Our results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log Discussion: This information will help improve GS efficiency and facilitate the application of GS.

Indexed as

GSoil contentprediction accuracyprotein contentsoybean

Identifiers

PMID36582644
PMCPMC9793221

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.